Termination Sequence Generation Circuits for Low-Density Parity-Check Convolutional Codes
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Bibliographic record
Abstract
Low-density parity-check convolutional codes (LDPC-CCs) complement their popular block-oriented counterparts and may be more suitable in certain communication applications. These include streaming voice, video, and packet switching networks. In order to use these codes efficiently we must generate termination sequences similar to those used in conventional convolutional codes. In this paper, we present a construction method for termination sequence generation circuits suitable for field-programmable gate arrays and application-specific integrated circuits. This method uses linear algebra to determine the termination sequence for a small number of states of the encoder and converts these solutions into a sequential circuit. Results are presented for several realizations of termination circuits for a (128,3,6) LDPC-CC
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it